Abstract：Convolutional neural networks make certain progress in medical image segmentation tasks due to their powerful feature extraction capabilities. However, the accuracy of edge segmentation still needs to be improved. To address this problem, a triplet-path network based on edge selection graph reasoning is proposed in this paper, including the target localization path, edge selection path and refinement path. In the target localization path, a multi-scale feature fusion module is designed to aggregate high-level features for the localization of lesion regions. In the edge selection path, an edge-selective graph reasoning module is constructed for edge screening of low-level features and graph reasoning to ensure the edge shape of the relevant lesion region. In the refinement path, a progressive group level refinement module is established to refine the structure information and details of different scale features. Moreover, a composite loss fusing weighted Focal Tversky loss and a weighted intersection over union loss is introduced to mitigate the effects of class imbalance. Experimental results on public datasets demonstrate the superior performance of the proposed method.
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